Bumshik Lee

Assistant Professor

Chosun University

South Korea

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Bumshik Lee is an Assistant Professor at Chosun University in South Korea. His research interests include image processing, machine learning, and medical imaging, with recent publications focusing on novel frameworks for automated image analysis and classification. Notable works include advancements in 3D tooth segmentation, osteoporosis diagnosis, and innovative image colorization methods. His contributions to the field also encompass real-time intrusion detection and multimodal human action recognition.

Bumshik Lee

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Articles (21)

DentSeg-EDA: A 3D tooth segmentation framework for CBCT images using enhanced dual attention

Multi-level feature enhancement and dual attention mechanisms for improved osteoporosis diagnosis

ColorFormer: A novel colorization method based on a transformer

Incomplete multi-view classification via graph neural network on heterogeneous graph

Comprehensive survey on deoldifying images and videos

A VVC Intra Rate Control With Small Bit Fluctuations Using a Lagrange Multiplier Adjustment

Transforming Color: A Novel Image Colorization Method

IDRandom-Forest: Advanced Random Forest for Real-Time Intrusion Detection

Multimodal Human Action Recognition Framework Using an Improved CNNGRU Classifier

BOLD-net: Brightness enhancement for old images using deep curve estimation and attention modules

AMCC-Net: An asymmetric multi-cross convolution for skin lesion segmentation on dermoscopic images

Combining Deep Convolutional Neural Networks With Stochastic Ensemble Weight Optimization for Facial Expression Recognition in the Wild

EFID: Edge-Focused Image Denoising Using a Convolutional Neural Network

Image Colorization Using Color-Features and Adversarial Learning

Multiclass Segmentation Using Teeth Attention Modules for Dental X-Ray Images

NG-GAN: A Robust Noise-Generation Generative Adversarial Network for Generating Old-Image Noise

MF2-Net: A multipath feature fusion network for medical image segmentation

SM-SegNet: A Lightweight Squeeze M-SegNet for Tissue Segmentation in Brain MRI Scans

Multiscale and Hierarchical Feature-Aggregation Network for Segmenting Medical Images

A Squeeze U-SegNet Architecture Based on Residual Convolution for Brain MRI Segmentation

An Analytic Transform Kernel Derivation Method for Video Codecs

Collaborators (2)

Myung Han Hyun

Munchurl Kim

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